OBJECTIVE:Cystic fibrosis-related diabetes (CFRD) is a common complication of cystic fibrosis (CF), increasing patient morbidity and mortality. Poor understanding of CFRD pathogenesis limits the development of targeted therapies to treat and/or prevent the disease. The aim of this study was to evaluate islet pathology, specifically, inflammation, amyloid deposition, and endocrine cell composition in subjects with CF with diabetes and with CF without diabetes. RESEARCH DESIGN AND METHODS:A retrospective analysis of archived pancreas tissue collected at autopsy was conducted using pancreas tissue from subjects with CF and diabetes (CFRD) (n = 18) and CF without diabetes (CF-no DM) (n = 17). Two cohorts of control non-CF subjects were identified, each matched to CFRD and CF-no DM subjects for age, sex, and BMI (non-CF older, n = 20, and non-CF younger, n = 20), respectively. Immunohistochemistry was performed to assess interleukin-1β (IL-1β) and islet hormone (insulin, glucagon, somatostatin, and pancreatic polypeptide) immunoreactivity; histochemistry was performed to quantify amyloid deposition. RESULTS:Islet IL-1β immunoreactivity was substantially increased in both CFRD and CF-no DM subjects compared with non-CF subjects and was common in young subjects with CF (≤10 years of age). In contrast, islet amyloid deposition was increased only in CFRD subjects. We also observe abnormal islet hormone immunoreactivity, characterized by increased glucagon immunoreactivity, in CF-no DM and CFRD subjects compared with non-CF subjects. CONCLUSIONS:These findings reveal novel molecular pathways and therapeutic targets underlying islet pathology in CF subjects and may be important in developing new approaches to treat CFRD.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.